New developments in DNA sequencing technology have spurred a tremendous increase in the use of sequencing to answer fundamental questions in biology and medicine. Whole- genome sequencing is being used to study cancer, to discover disease-causing gene variants in patient genomes, and to study human genetic diversity. Numerous WGS projects are being launched for species whose genomes have not yet been sequenced. Sequencing of messenger RNA through RNA-seq has led to an explosion of projects to characterize transcribed genes in multiple cell types and in many species, and simultaneously to discover new genes and new splice variants of known genes. These sequencing-based studies generate enormous amounts of data, which in turn require sophisticated, efficient, and innovative new algorithms that will make it possible to assemble these genomes and identify their gene content. We propose to develop new cloud-computing based assembly algorithms to assemble genomes from short reads generated by the latest sequencing technologies. In parallel, we will continue to improve our existing assemblers, extending them to handle new and diverse data types, including 3rd-generation sequences. We will also reach out to outside groups to help them assemble novel species, modifying our software as needed and continuing to push the limits of assembly technology. One of the most exciting recent technology developments in the gene finding arena is RNA- seq, a new protocol for capturing and sequencing the mRNA in a cell. This technique is well on its way to replacing both conventional EST sequencing as a method for capturing transcribed protein-coding genes, and microarray hybridization experiments for measuring transcript levels. We propose to develop new algorithms to take advantage of the flood of new RNA-seq data that has begun to appear. We have already developed two new algorithms, TopHat and Cufflinks, for RNA-seq analysis, which are the first to be able to discover previously unknown splice sites and isoforms. These tools, enhanced with new features to handle a wider variety of sequence data, form the basis of our plans to develop integrated gene finders that can identify novel genes, novel isoforms of known genes, and fusion genes, and to include these methods in a genome annotation pipeline.